Data management apparatus for comparing patient data with ailment archetypes to determine correlation with established ailment biomarkers

ABSTRACT

The Patient Data Management System operates under the control of a physician to implement a patient-specific instance of the apparatus which is capable of accessing at least one data manipulation module, each defining at least one process for transforming patient medical data pursuant to a predefined schema. A patient medical data processor is responsive to the physician selecting a set of patient medical data, at least one of the data manipulation modules, and an order of applying the selected data manipulation modules to the selected patient medical data for processing the selected patient medical data using the selected data manipulation modules in the selected order to identify data indicative of a predetermined condition in the selected patient medical data. A display is available for presenting a visualization of the data indicative of a predetermined condition in the selected patient medical data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 12/567,249 filed on Sep. 29, 2009, which application is acontinuation-in-part of U.S. patent application Ser. No. 12/505,185filed on Jul. 17, 2009. This application is also related to anapplication filed concurrently herewith titled “Medical Apparatus ForCollecting Patient Electroencephalogram (EEG) Data” and an applicationfiled concurrently herewith titled “Patient Data Management ApparatusFor Comparing Patient EEG Data With Ailment Archetypes To DetermineCorrelation With Established Ailment Biomarkers.” The foregoingapplications are hereby incorporated by reference to the same extent asthough fully disclosed herein.

FIELD OF THE INVENTION

This invention relates to medical data processing systems and, inparticular, to a system that enables a physician to implement apatient-specific medical diagnostic tool which uses known sets ofmedical and/or psychological data, which are correlated with patientdata, to identify potential patient ailments or conditions.

BACKGROUND OF THE INVENTION

There are numerous existing medical test and analysis systems, many ofwhich are ailment-specific, for either generating patient test data formanual review by a treating physician or for analyzing collected patienttest data to determine whether the patient suffers from a certainailment or condition. These existing test and analysis systems areailment-specific and simply process the collected patient data toprovide the treating physician with a presentation of the data which canbe interpreted by the treating physician to determine whether thepatient suffers from the condition the test is designed to detect and/orwhether the patient falls into predefined relevant risk groups.

Some of the latest generations of medical devices have transitioned frombasic physiological measurement (that requires a trained interpretation)to a formal ailment diagnosis. The deterministic approach of thesemedical devices appropriately requires significant regulatory oversightto ensure the reliability and validity of the technology and theaccuracy of the formal ailment diagnosis. While these objectivediagnoses produced by the medical devices represent significantcommercial value, they come at significant cost with respect to time andcosts of empirical clinical trials, including the risk of futurecontradictory or displacing research. In the case of a conflict betweencommercial diagnostics and new research, the community of diagnosticiansis left with a less than optimal solution and often is presented with aprofound uncertainty resulting in a stalled decision in patientpoint-of-care situations. In the end, rapid change in the associatedmedical science can produce paralysis when physicians suspect theautomated ailment diagnoses are using outdated informational andanalysis sources.

A problem with physician diagnosis of ailments is that the validity ofthe results obtained by using this process is predicated on the treatingphysician both having sufficient knowledge of the ailment and also beingable to identify anomalies in the patient test data, which anomalies canbe subtle indicators. Medical schools are traditionally responsible fortraining physicians on the diagnostic utility of physiologicalmeasurements, with more training required for measures that don't have asimple FDA-approved binary solution. This education rightfully focuseson historic studies that provide the interpretation of medical deviceand laboratory data. These interpretations rely on the integrity andtransparency of peer-reviewed scientific journals and medical texts fromwhich a physician would base their diagnostic and treatment decisions.The primary source for these materials (both in medical school and forpracticing physicians) is public and private medical libraries, each ofwhich supports a standard for the peer review and medical validity oftheir content. Research on new medical devices and diagnosticinterpretation are added to these libraries daily, representing anexplosion in the amount of scientific studies potentially relevant tophysicians' patient populations. Unfortunately for the generalpractitioners (and even specialists), the volume of new research isdifficult to assimilate into the point-of-care without the context of agiven patient measurement, and these results are often contradictory.Understanding that the ability to aggregate and interpret a variety ofpatient diagnostic measurements is the responsibility of the physician;however, there is a limit to the amount of raw data these highly trainedprofessionals can integrate. Furthermore, there are little knownailments that manifest themselves in a manner that closely mimic otherailments, thereby making it difficult to diagnose these ailments, sincethe physician only sees patient symptoms and patient-specific testresults.

A further problem is that there is a limitation in this process in thata human can only process a certain limited amount of data; and thetreating physician, no matter how skilled, can fail to identify theconvergence of a number of trends in the patient test data or acollection of seemingly unrelated anomalies. This is especially truewhen there are a number of existing patient-specific test results, suchas: past ailment-specific test results, present ailment-specific testresults, test results directed to test for other ailments, patientdemographic and physical data, including the patient's history ofmedications, and the like.

A further problem derives from the fact that 21st century medicineincreasingly utilizes Electronic Medical Records (EMRs) in patient care.An Electronic Medical Record is a computerized legal medical record,which is part of a localized health information system that allowsorganized storage, retrieval, and manipulation of information. TheAmerican Recovery and Reinvestment Act of 2009 identifies threeimportant goals in the deployment of Electronic Medical Records: (1)improved efficiency in insurance payments, (2) reductions in duplicatetesting, and (3) “to guide medical decisions at the time and place ofcare”.

In the past, the Food and Drug Administration has not actively regulatedElectronic Medical Records. However, due to the advent of new, morepowerful data storing and organizing network systems that can aggregatemany thousands of often anonymized records, Electronic Medical Recordswill soon be capable of creating hitherto nonexistent clinicaldatabases. The problem arises when those databases begin to be used toguide medical decision-making regarding the treatment received by manypatients at the time and place of care. As such, the validity of thatguidance becomes a major issue of contention for the Food and DrugAdministration, whose mandate is to assess the safety and effectivenessof all registered medical devices.

Naturally, a major debate has opened up regarding the question ofregulatory oversight; namely, the identity of the authorities whodetermine the validity of the conclusions derived from the data. In thecase of private corporations owning the Electronic Medical Recordsnetworks, they will not only “own” the data; they will also be incentedto mine the data in such a way as to direct interpretations of it tophysicians. Suspicions will certainly rise regarding the validity ofthese interpretations due to profit motives.

In opposition to private corporations, government agencies, includingONCHIT and the Food and Drug Administration will see the need toregulate these interpretations through the government's oversight. Thisprocess is both very slow and often uncertain and may cause harmfuldelays in understanding the guidance potential of the data. Furthermore,much of the data interpretation, although potentially very useful toclinical analysis at the point of care, may not meet the standardgovernment agencies' need to certify it as effective, thereby limiting aphysician's ability to utilize a potentially life-saving solution.Further problems will occur when this data begins to evolve in itsinterpretive guidance. As more data is collected, the guidance itprovides may change very rapidly, and the government oversight processwill not be able to keep up. This scenario could potentially create aconfusing state of affairs between regulatory bodies and healthcarefacilities.

Historically, these problems have been solved through the peer-reviewedsystem of scientifically researched data analysis and publication. Themajor benefits of this system are the constant revisions that thescientific process provides along with the self-policing effect againstdiscredited methods that peer-reviewed publications provide. However,this process relies entirely on an open source data system that allowsscientists full access to common data warehouses for independentinterpretation. When that access is limited, invariably so are theresults of the subsequent studies. The only flaw in this system is thatthe process of peer-reviewing and publication is slow; and much of thenew interpretations take too long to get to the point of care, thuseliminating one of the major goals of the new Electronic Medical Recordstechnology objectives.

Thus, there is presently no system which enables the treating physicianto effectively monitor and process multiple sets of patient data andcustomize the analysis of this collected patient-specific data for theindividual patient to cover ailments as specified by the physician (orunspecified), all in conjunction with the ability to dynamically accessa Digital Library which provides the treating physician with resourcematerials, including control and control data sets for patients.

BRIEF SUMMARY OF THE INVENTION

The above-described problems are solved and a technical advance achievedby the present Data Management Apparatus For Comparing Patient Data WithAilment Archetypes To Determine Correlation With Established AilmentBiomarkers (termed “Patient Data Management System” herein), whichenables the treating physician to monitor one or more sets of patientdata and customize, using patient- or group-specific customization, theanalysis of this collected patient-specific data for the individualpatient to cover one or more ailments, all in conjunction with a DigitalLibrary which provides the treating physician with resource materials,including control data sets for patients.

In this description, the term “physician” (also termed “diagnostician”or “clinician”) is intended to include anyone who performs a diagnosticfunction, reviews data about a patient, and correlates that data withknown ailments to provide the patient with a diagnosis of their presentstate of health. In addition, the term “ailment” (also termed“condition” herein) is used in the general sense to represent anymedical or psychological or physiological condition or problem thataffects, or may in the future affect, a patient, whether or not it islife- or health-threatening.

The Patient Data Management System operates under the control of aphysician to implement a patient-specific instance of the apparatuswhich is capable of accessing at least one data manipulation module,each defining at least one process for transforming patient medical datapursuant to a predefined schema. A patient medical data processor isresponsive to the physician selecting a set of patient medical data, atleast one of the data manipulation modules, and an order of applying theselected data manipulation modules to the selected patient medical datafor processing the selected patient medical data using the selected datamanipulation modules in the selected order to identify data indicativeof a predetermined condition in the selected patient medical data. Adisplay is available for presenting a visualization of the dataindicative of a predetermined condition in the selected patient medicaldata.

This apparatus can include a link to a Digital Library for providingaccess to a plurality of information sources which relate tointerpreting patient medical data and possible ailments associated withpatient medical data, as well as data characterization modules forcalculating control variations of a set of patient medical datacollected from and about an identified patient, with patient medicaldata of a base set of control data to identify anomalies in a set ofpatient medical data.

Furthermore, the Patient Data Management System can include a DigitalLibrary interface module, which responds to receipt of the set ofpatient medical data collected from and about an identified patient aswell as identified anomalies calculated by the data characterizationmodule relating to the set of patient medical data, by searching theDigital Library with physician-defined search criteria to locateinformation sources relating to the set of patient medical data andinterpretations of the identified anomalies calculated by the datacharacterization module relating to the set of patient medical data. Aninformation access module provides an authorized physician with accessto the information sources returned by the Digital Library interfacemodule and relating to the set of patient medical data.

The Patient Data Management System can also access shared databaseswhich serve a group of physicians and health care facilities to enablethe physician to retrieve multiple sets of patient data as well as toaccumulate point-of-care knowledge in a quickly-expandingmedical-discovery environment. The physician not only can interpretmedical data to provide an improved diagnosis of existing and/or futureailments by accessing the Digital Library, but also can put this databack into the system for further analysis. The patient-specific data canbe configured into a patient anonymous form for this purpose to enableother physicians to benefit from the data without infringing on theprivacy of the patient. The Patient Data Management System therebycreates a medical-advancement environment—the library takespatient/physician data from multiple sources including outcomes,incorporates all of the data into new knowledge, and puts theinformation back to the physician at a point-of-care locus.

An additional benefit of the Patient Data Management System is that thediscovery of an outcome need not be linked to a predeterminedphysician-defined hypothesis—all the data is parameterized, and theparameters are allowed to “wander”, where parameters are tested ascorrelations against the existing environment of all known medicaloutcomes. In this paradigm, the successful correlations survive as newmedical discoveries which couple symptoms and/or anomalies and/or testdata to an ailment. This is especially pertinent in the situation wherelittle-known ailments are present and the physician-defined hypothesiscould inadvertently exclude these little-known ailments due to thephysician unknowingly biasing the analysis.

Thus, the Patient Data Management System provides diagnosticcapabilities heretofore unknown in the medical profession by linking aDigital Library to the physician and patient-specific data as definedand moderated by the physician.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of the present Patient Data Management Systemand an environment in which it is operational;

FIG. 2 is a block diagram illustrating the components of the DigitalLibrary;

FIG. 3 is block diagram illustrating the components of the PhysicianApplication;

FIG. 4 is a flow chart illustrating operations for setting up orupdating a Physician Application interface;

FIG. 5 illustrates the concept of selecting predetermined ailmentpatient data patterns;

FIG. 6 is a block diagram of an EEG Processing Unit and an environmentin which it is operational;

FIG. 7 is a block diagram illustrating an alternative embodiment of theEEG Processing Unit which makes use of an exoskeleton in the EEGProcessing Unit;

FIG. 8 is a block diagram illustrating an implementation of the EEGTransducer Placement System used in the EEG Processing Unit;

FIG. 9 is a block diagram illustrating an example electrode placementfor gathering EEG data;

FIG. 10 illustrates a circuit diagram of the elements incorporated inthe electrodes and the associated communications controller;

FIG. 11 illustrates in flow diagram form the operation of the processorincorporated in the electrodes;

FIGS. 12A and 12B illustrate an example of EEG data that may be gatheredduring an EEG test;

FIG. 13A is a screen shot of EEG data presented in a spectral array;

FIG. 13B is a screen shot of topographic maps of the raw EEG data;

FIG. 14A illustrates a screen shot showing a compressed spectral arrayof raw EEG data;

FIG. 14B illustrates a screen shot of a trend analysis of the raw EEGdata;

FIG. 15 illustrates the operation of the Patient Data Management Systemin manipulating collected patient data; and

FIG. 16 illustrates in flow diagram form the operation of the PatientData Management System.

DETAILED DESCRIPTION OF THE INVENTION

The Patient Data Management System operates under the control of aphysician to implement a patient-specific instance of the apparatuswhich is capable of accessing at least one data manipulation module,each defining at least one process for transforming patient medical datapursuant to a predefined schema. A patient medical data processor isresponsive to the physician selecting a set of patient medical data, atleast one of the data manipulation modules, and an order of applying theselected data manipulation modules to the selected patient medical datafor processing the selected patient medical data using the selected datamanipulation modules in the selected order to identify data indicativeof a predetermined condition in the selected patient medical data. Adisplay is available for presenting a visualization of the dataindicative of a predetermined condition in the selected patient medicaldata.

Based upon this statistical analysis, a Digital Library interface modulesearches the Digital Library for information sources relating to the setof patient medical data and interpretations of the identified anomalies.There can also be an information access module which provides anauthorized person, such as a physician, with access to the informationsources returned by the Digital Library interface module and relating tothis set of patient medical data. The physician can use this informationat the point-of-care on an identified patent according to the level ofautomation built by that physician.

In this manner, the physician's diagnosis of a patient ailment is datadriven rather than physician-diagnosis driven. The collected datarelating to a patient can be mapped to biomarkers selected by thephysician to determine a closeness of match with the physician-selectedbiomarkers. In this manner, the physician can look for analogouspatterns of data which are indicative of an ailment with which thepatient is afflicted. The associated information sources then can beretrieved from the Digital Library by the physician to provide in-depthailment and treatment information. This enables a physician to abandonthe traditional path of selecting a diagnosis based on vague patientsymptoms and then prescribing a treatment. If the treatment relieves thepatient's symptoms, then the treatment is deemed successful. However,there is no positive determination that the ailment is properlyidentified or the treatment is the proper one for the underlyingailment. This is the weakness of traditional medical care, especiallywhen the physician is faced with an atypical ailment, which thephysician may never have previously encountered. By driving thediagnosis using the collected patient data, the physician is empoweredto consider a wide variety of possible ailments without having to expendexcessive time, order extensive diagnostic tests, or repeatedly cyclethrough successive unsuccessful treatments.

System Architecture—Patient Data Management System

Communication Environment

FIGS. 1 and 2 are block diagrams of the present Patient Data ManagementSystem and an environment (described in more detail in co-pending U.S.patent application Ser. No. 12/505,185) in which the present PatientData Management System is typically operational. In particular, thecommunications environment is adaptable; and a physician 314 who isequipped with at least one electronic device (such as computer 311,printer/fax/scanner 312, cell phone 313, and the like—collectivelytermed “physician equipment 310”) can be connected via a communicationmedium 115 (such as wire line, cable television, satellite, cellular,and the like) to an Internet Service Provider (ISP) 117 whichinterconnects the physician with a data communication network 118(termed “Internet” herein) and thence to the Patient Data ManagementSystem 100. Alternatively, physician 214 who is equipped with at leastone electronic device (such as computer 211, printer/fax/scanner 212,cell phone 213, and the like—collectively termed “physician equipment210”) can be directly connected via a communication medium 116 (such aswire line, cable television, satellite, cellular, and the like) to thePatient Data Management System 100. A final configuration is physician114 who is equipped with at least one electronic device (such ascomputer 111, printer/fax/scanner 112, cell phone 113, and thelike—collectively termed “physician equipment 110”) is directlyconnected “on-site” to the Patient Data Management System 100. Theseconfigurations are exemplary, and the following description is directedto the functionality provided by the Patient Data Management System 100which operates independent of the access architecture which is used.

Patient Data Management System

The Patient Data Management System 100 operates one or more DatabaseServers 104 to provide a secure environment for the storage andprocessing of data associated with the applications described herein.The Database Servers 104 (also referred to as “databases”) can beimplemented by a cluster of servers and interface with the DigitalLibrary 105 which serves to store mass quantities of medical informationas described below. A Firewall 103 is also provided to prevent access tothe Database Server 104 except for the authorized applications. Theinterface server 101 and the WEB server 102 respond to requests frombrowsers and process the request and store and retrieve data on theassociated Database Server 104 as required.

The Patient Data Management System 100 includes a Digital Library 105for providing access to a plurality of information sources which relateto interpreting patient medical data and possible ailments associatedwith patient medical data, as well as a Data Characterization Module 106for calculating control variations of a set of patient medical datacollected from and about an identified patient with patient medical dataof a base set of control data to identify anomalies in a set of patientmedical data. Furthermore, the Database Servers 104 include a DigitalLibrary Interface Module 107, which responds to receipt of the set ofpatient medical data collected from and about an identified patient aswell as identified anomalies calculated by the Data CharacterizationModule 106 relating to the set of patient medical data, by searching theDigital Library 105 for information sources relating to the set ofpatient medical data and interpretations of the identified anomaliescalculated by the Data Characterization Module 106 relating to the setof patient medical data. The Digital Library Interface Module 107 alsocontains physician selected bio-markers, ailment characteristics, searchcriteria, and rules engines that highlight conditions of particularconcern to the physician. An Information Access Module 108 provides anauthorized physician with access to the information sources returned bythe Digital Library Interface Module 107 and relating to the set ofpatient medical data.

In addition to Digital Library 105, the physician can have access toshared medical records which reside on Medical Record Database 109,which may be part of Patient Data Management System 100 or managed as aseparate entity. The Medical Record Database 109 can be a databaseshared among a plurality of physicians and health care facilities whichserve a region or patient population. As is the norm, access to thePatient Data Management System 100 and Medical Records Database 109 isregulated to admit only authorized personnel via a secure accessprotocol as is known in the art. The Medical Records Database 109includes a WEB server 109C, a Firewall 109B, and the Records Database109A.

Database Server

Database Server 104, as shown in block diagram form in FIG. 2, consistsof the processing engine which executes the processes described hereinand which manages access to the Digital Library 105 and the variousdatabases 260-280. In this regard, Control Database 260 contains control(typically normal) data sets for subject data, where the control groupcan be a normal control or an ailment-specific control also which bothmake up what is referred to as archetypal datasets. This data comprisessets of data which represent the control measurement data of variouscharacteristics of subjects that are taken from readings on varioussubjects, with the data typically being parsed by subject category andsubject characteristics as is well known in the art. CorrelativeDatabase 270 consists of the results of correlating patient-specificdata with selected data sets contained in the Control Database 260. Thecorrelation data represents detected anomalies which can be used by thetreating physician to identify one or more ailments which affect thepatient. This correlation data can be tied to a resultant diagnosis andtreatment methodology (with follow-up data indicative of results orsuccessive correlation results) and can be shared among physicians toenable other physicians to benefit from these results. The patient datais anonymized to ensure privacy of the patient, with typically only therelevant patient physical, socioeconomic, psychological, and demographicdata being tied to the results. Data Mining Database 280 consists of theanalysis data which are generated by physicians using the Patient DataManagement System 100 and represent the results of data and literatureanalysis operations, including past searches and analyses performed byphysicians. The Data Mining data, therefore, is the log of experientialinformation that is shared on a peer-to-peer basis by the physicians whoaccess the Customized Medical Diagnostic Apparatus 100.

Thus, there are two aspects of the Patient Data Management System 100which should be noted here. The first aspect is the patient-agnosticrepository of relevant published literature 250 and indices 210-240stored in the Digital Library 105, which are representative of theresources available to the physicians to use in their various diagnosticprocesses as well as for their general professional education. Thesecond aspect of the Patient Data Management System 100 is the sets ofdata stored in the databases 260-280, representative ofphysician-generated and controlled information which is eitherpatient-specific or control in nature. The physician generates thesearch strategies, analysis tools, correlation steps, and data miningoperations to produce a patient-specific analysis, treatment, andongoing care management regimen. Thus, this second aspect isrepresentative of both physician-specific and peer-to-peer research andanalysis results which collectively enhance the knowledge set availableto the physicians who use the Patient Data Management System 100 intheir practices.

Digital Library

The Digital Library 105 contains digitized information (PublishedLiterature 250) such as books, scientific research, manuscripts, videos,audio files, etc., that can be used by the physician to researchailments, conditions, and other related information. The Digital Library105 can also include a plurality of indices, such as: a Trait ScaleIndex 210, a Diagnostic Index 220, a Treatment Index 230, and/or aPredictive Index 240 that can aid a physician in explaining possibleinterpretations of patient data and making improved diagnoses. Theseindices contain data relations, such as the biomarkers described below,that enable the physician to statistically compare patient-specific testresults to a base group of control subjects to obtain correlation datawhich indicate the probability of the presence of a particularpsychological or physiological ailment or condition in the patient.

Trait Scale Index 210 is a collection of data relations and possiblydistributions of those data relations as found in members of the controldatabase that have been identified from literature. Diagnostic Index 220is a set of data relations that have been correlated with diagnoses froma physician. These correlations are developed based on data accessedthrough the aggregated patient database stored in Data Mining Database270. Diagnostic Index 220 may also include distributions of the datarelations and may also be computed information of control subject datafound in Control Database 260. Out-of-variance data relations foundduring the statistical analysis can be searched for in Diagnostic Index220 in order to aid a physician in determining the ailment of a patient.Treatment Index 230 is a collection of data relating to treatments whichhave proven effective for patients with similar statisticalcharacterizations of the collected patient data. Predictive Index 240includes a set of data relations that have been correlated betweenearlier collected patient data and a current diagnosis from a physician.For example, a patient may go to a physician and have routine medicaltests performed throughout his lifetime. In his seventies, he takes amedical test and is diagnosed with Alzheimer's. Predictive Index 240 iscreated by generating correlations between this and other patients'earlier test data and the current diagnosis of Alzheimer's to find earlyindicators.

Digital Library 105 has access to multiple publications or other typesof Published Literature 250 relating to interpreting medical data andpossible ailments associated with medical data. Examples of the types ofpublications and published information stored in or accessible to theDigital Library include, but are not limited to: books, scientificresearch articles, scientific research manuscripts, videos, audio files,tables, and/or summaries of published information, desk references, andthe like.

Digital Library 105 can also store Physician Applications which can beloaded directly into the digital terminals used by a physician as isdescribed below. For example, a specialist in the field of depressioncould create a Physician Application that has data relations thespecialist would be interested in using during the statisticalcharacterization of the relevant patient data.

Data Characterization Module

Data Characterization Module 106 is a process which typically executeson the Database Server 104 and which calculates control variations of aset of patient medical data collected from and about an identifiedpatient with patient medical data of a base set of control data toidentify anomalies in a set of patient medical data.

Digital Library Interface Module

Digital Library Interface Module 107 is a process which typicallyexecutes on the Database Server 104 and which responds to receipt of theset of patient medical data collected from and about an identifiedpatient as well as identified anomalies calculated by the DataCharacterization Module 106 relating to the set of patient medical data,by searching the Digital Library 105 for information sources relating tothe set of patient medical data and interpretations of the identifiedanomalies calculated by the Data Characterization Module 106 relating tothe set of patient medical data.

Information Access Module

Information Access Module 108 is a process which typically executes onthe Database Server 104 and which provides an authorized physician withaccess to the information sources returned by the Digital LibraryInterface Module 107 and relating to the set of patient medical data.

Physician Application

As noted above, the physicians access the Patient Data Management System100 either via Server 101 or via WEB portal server 102. The physicians'access is typically via a Physician Application 150 (shown in FIG. 3)which executes on a physician digital terminal 111, 211, and 311. ThePhysician Application 150 consists of various processes which enablecommunication with the above-noted elements of the Patient DataManagement System 100 and which enable the physician to view andgenerate reports and data output used in the diagnosis and treatment ofthe patient's ailments.

FIG. 3 is block diagram illustrating a typical Physician Application 150which executes an application which is part of the Patient DataManagement System 100 to receive and process patient test data from andfor a specific patient. The Physician Application 150 shown in FIG. 3includes the following components which are part of Patient DataManagement System 100: WEB Portal Application 335, Patient Test DataModule 340, Electronic Medical Records Access Module 350, Medical DataInput Module 360, Predicative Ailment Report Module 370, TreatmentReport Module 380, and Physician Diagnosis Module 390. Memory Store 310can have stored thereon local versions of a control database, acorrelative database, a patient test database, and a data miningdatabase. FIG. 5 illustrates the concept of selecting predeterminedailment patient data patterns, and FIG. 16 illustrates in flow diagramform the operation of the Patient Data Management System 100.

Web Portal Application 355 may be used by Physician Application 150 toconnect to the Patient Data Management System 100. The PhysicianApplication 150 can transfer patient data, such as: patient test data,data relations, medical data, and the like to the Patient DataManagement System 100 and receive reports and statisticalcharacterization results from the Patient Data Management System 100.Web Portal Application 335 is able to encrypt and decrypt theinformation that is being sent and received.

Patient Test Data Module 340 provides an interface between the DataAcquisition and Display Module 120 and Physician Application 150.Patient Measurement Data Module 340 also translates any requests formore patient test data from the Physician Application 150 into a formatrequired by the Patient Data Management System 100 and translates and/ordirects incoming requests and/or data to the appropriate module orapplication within the Physician Application 150. Patient Test DataModule 340 is a receiving module that is configured to receive sets ofpatient test data relating to a test subject and store the sets ofpatient test data in a patient test database.

Once patient test data is received through Patient Test Data Module 340,the data may be associated with sets of data received through ElectronicMedical Records Access Module 350. Electronic Medical Records AccessModule 350 provides an interface between Electronic Medical RecordsDatabases 170 and Physician Application 150. Electronic Medical RecordsAccess Module 350 translates any requests for electronic medical recordsfrom the Physician Application 150 into a format required by thedestination component. Similarly, Electronic Medical Records AccessModule 350 is able to translate and/or direct incoming requests (e.g.,requests for passwords or other authentication) and/or data to theappropriate module or application within the Physician Application 150.

Predictive Ailment Report Module 370 and Treatment Report Module 380interface with the Electronic Medical Records data of the patient,Storage Databases 170, and/or Analysis Platform 160 (shown in FIG. 5) torequest information about current and past treatments and previouslygenerated predictive ailment reports.

Physician Diagnosis Module 390 generates physician interface screen(s)that allow the physician to input a diagnosis. Physician DiagnosisModule 390 provides for the entry of a diagnosis, and PhysicianDiagnosis Module 390 can interface with the Analysis Platform 160 toretrieve a set of ailment “filters” in the form of the biomarkersdescribed below for the physician to choose from in order to process thepatient test data. If the physician likes one of those ailment filters,the physician can select it. If not, the physician is able to enter adifferent diagnosis through the physician interface screen options.

EEG Processing Unit

In order to illustrate the operation of the Patient Data ManagementSystem 100, the example of an EEG Processing Unit is used to demonstratethe physician's ability to selectively process collected patient data.The EEG Processing Unit is described in co-pending application titled“Medical Apparatus For Collecting Patient Electroencephalogram (EEG)Data” and comprises a semi-rigid framework which substantially conformsto the head of the Patient. The framework supports a set of electrodesin predetermined loci on the Patient's head to ensure proper electrodeplacement. The EEG Processing Unit includes automated connectivitydetermination apparatus which can use pressure-sensitive electrodeplacement to ensure proper contact with the Patient's scalp and alsoautomatically verifies the electrode placement via measurements of theelectrode impedance through automated impedance checking. In addition,the EEG Processing Unit can include optional automated electrodemovement or rotation apparatus to clean the skin of the Patient tooptimize the electrode contact with the Patient's scalp as indicated bythe measured impedance.

The voltages generated by the electrodes are amplified and filteredbefore being transmitted to an analysis platform, which can be aPhysician's laptop computer system, either wirelessly or via a set oftethering wires. The EEG Processing Unit includes an automaticartifacting capability which identifies when there is sufficient cleandata compiled in the testing session. This process automaticallyeliminates muscle- or other physical-artifact-related voltages. Cleandata, which represents real brain voltages as opposed to muscle- orphysical-artifact-related voltages, thereby are produced. The automaticartifacting capability optionally includes an automatic Patient motionartifacting capability via an accelerometer that produces dataindicative of Patient movement, which enhances the identification ofaccurate data.

Source of EEG Activity

The electrical activity of the brain can be described in spatial scalesfrom either the currents that are generated within a single dendriticspine or the potentials that the EEG records from the Patient's scalp.Neurons, or nerve cells, are electrically active cells which areprimarily responsible for carrying out the brain's functions. Neuronscreate action potentials, which are discrete electrical signals thattravel down axons and cause the release of chemical neurotransmitters atthe synapse, which is an area of near contact between two neurons. Thisneurotransmitter then activates a receptor in the dendrite or body ofthe neuron that is on the other side of the synapse, the post-synapticneuron. The neurotransmitter, when combined with the receptor, typicallycauses an electrical current within the dendrite or body of thepost-synaptic neuron. Thousands of post-synaptic currents from a singleneuron's dendrites and body then sum up to cause the neuron to generatean action potential. This neuron then synapses on other neurons, and soon. A typical adult human EEG signal is about 10 μV to 100 μV inamplitude when measured from the scalp.

An EEG reflects correlated synaptic activity caused by post-synapticpotentials of cortical neurons. The ionic currents involved in thegeneration of fast action potentials may not contribute greatly to theaveraged field potentials representing the EEG. More specifically, thescalp electrical potentials that produce EEGs are generally thought tobe caused by the extracellular ionic currents caused by dendriticelectrical activity, whereas the fields producingmagneto-encephalographic signals are associated with intracellular ioniccurrents.

The electric potentials generated by single neurons are far too small tobe picked up by an EEG. Therefore, EEG activity always reflects thesummation of the synchronous activity of thousands or millions ofneurons that have similar spatial orientation, radial to the scalp.Currents that are tangential to the scalp are not picked up by the EEG.Therefore, the EEG benefits from the parallel, radial arrangement ofapical dendrites in the cortex. Because voltage fields fall off with thefourth power of the radius, activity from deep sources is more difficultto detect than currents near the skull.

Scalp EEG activity shows oscillations at a variety of frequencies.Several of these oscillations have characteristic frequency ranges andspatial distributions and are associated with different states of brainfunctioning (e.g., waking and the various sleep stages). Theseoscillations represent synchronized activity over a network of neurons.The neuronal networks underlying some of these oscillations areunderstood, while many others are not. Research that measures both EEGand neuron spiking finds the relationship between the two is complex,with the power of surface EEG only in two bands, that of gamma anddelta, relating to neuron spike activity.

Clinical Use

A routine clinical EEG recording typically lasts between 20 and 30minutes (plus preparation time) and usually involves recording fromscalp electrodes which are manually placed in predetermined locations onthe scalp of the Patient by the EEG test operator. A routine EEGtypically is used in the following clinical circumstances, as ordered bya Physician to diagnose an Ailment and to:

-   -   distinguish epileptic seizures from other types of spells, such        as psychogenic non-epileptic seizures, syncope (fainting),        sub-cortical movement disorders, and migraine variants;    -   differentiate “organic” encephalopathy or delirium from primary        psychiatric syndromes such as catatonia;    -   serve as an adjunct test of brain death;    -   prognosticate, in certain instances, in Patients with coma; and    -   determine whether to wean Patients off anti-epileptic        medications.

Additionally, EEG may be used to monitor certain procedures:

-   -   to monitor the depth of anesthesia;    -   as an indirect indicator of cerebral perfusion in carotid        endarterectomy; and    -   to monitor amobarbital effect during the Wada test.

An EEG also can be used in intensive care units for brain functionmonitoring to:

-   -   monitor for non-convulsive seizures/non-convulsive status        epilepticus;    -   monitor the effect of sedative/anesthesia in patients in        medically induced coma (for treatment of refractory seizures or        increased intracranial pressure); and    -   monitor for secondary brain damage in conditions such as        subarachnoid hemorrhage (currently a research method).

In conventional scalp EEGs, the recording is obtained by the EEG testoperator manually placing electrodes on the scalp with a conductive gelor paste, usually after manually preparing the scalp area by lightabrasion to reduce impedance due to dead skin cells. Many systemstypically use electrodes, each of which is attached to an individualwire. Some systems use caps or nets into which electrodes are embedded;this is particularly common when high-density arrays of electrodes areneeded.

Electrode locations and names are specified by the International 10-20system for most clinical and research applications and must be preciselyfollowed by the EEG test operator in order to collect valid EEG data.This system ensures that the naming of electrodes is consistent acrosslaboratories. In most clinical applications, 19 recording electrodes(plus ground and system reference) are used. A smaller number ofelectrodes are typically used when recording EEGs from neonates.Additional electrodes can be added to the standard set-up when aclinical or research application demands increased spatial resolutionfor a particular area of the brain.

Each electrode is connected to one input of a differential amplifier(one amplifier per pair of electrodes); a common system referenceelectrode is connected to the other input of each differentialamplifier. These amplifiers amplify the voltage between the activeelectrode and the reference. In analog EEGs, the signal is thenfiltered, and the EEG signal is output as the deflection of pens aspaper passes underneath. Most EEG systems are digital, and the amplifiedsignal is digitized via an analog-to-digital converter, after beingpassed through an anti-aliasing filter. Analog-to-digital samplingtypically occurs between 256 Hz and 512 Hz in clinical scalp EEGs;sampling rates of up to 20 kHz are used in some research applications.

The digital EEG signal is stored electronically and can be filtered fordisplay. Typical settings for the high-pass filter and a low-pass filterare 0.5-1 Hz and 35-70 Hz, respectively. The high-pass filter typicallyfilters out slow artifact, such as electrogalvanic signals and movementartifact, whereas the low-pass filter filters out high-frequencyartifacts, such as electromyographic signals. An additional notch filteris typically used to remove artifact caused by electrical power lines(60 Hz in the United States and 50 Hz in many other countries).

EEG Processing Unit

FIG. 6 is a block diagram of the present EEG Processing Unit and anenvironment in which it is operational. The EEG Processing Unit 600includes “helmet-like” frame apparatus 601, which is typicallysemi-rigid in nature, conforms to the head of the Patient 602, andsupports a set of electrodes 603-1 to 603-N, in predetermined loci, onthe Patient's head to ensure proper electrode placement. Properelectrode placement is critical to the collection of accurate data toenable the Physician to obtain readings of the above-mentioned Waves andto distinguish anomalies in these Waves from normal patterns. Inaddition, associating electronics with the sensors in the EEG ProcessingUnit enables signal sampling and signal processing close to the sourceof the EEG signals so that the data that is transmitted for storage andreview by the Physician is relatively noise-free before it leaves theEEG Processing Unit.

FIG. 7 is a block diagram illustrating an alternative embodiment of theEEG Processing Unit 600 which makes use of an exoskeleton 701 as analternative to the “helmet” 601 design shown in FIG. 6. As with the“helmet” design, the exoskeleton 701 conforms to the head of the Patientand is a semi-rigid framework which supports a set of electrodes 703-1to 703-N, in predetermined loci, on the Patient's head to ensure properelectrode placement.

EEG Electrode Placement System

FIG. 8 is a block diagram illustrating an implementation of the EEGElectrode Placement System 800 used in the EEG Processing Unit 600. TheEEG Electrodes 801 comprise sensitive electronics, as shown inadditional detail in FIG. 10, which includes automated connectivitydetermination apparatus which uses pressure-sensitive electrodeplacement to ensure proper contact with the Patient's scalp and alsoautomatically verifies the electrode placement via measurements of theelectrode impedance through automated impedance checking. In particular,the EEG Transducer Placement System 800 includes an Electrode PressureMechanism 802 that, upon placement of the EEG Processing Unit 600 on thehead of the Patient 602, is activated by the EEG test operator to applypressure to the individual EEG Electrodes 801 which are attached to theexoskeleton 601 or 701 thereby to ensure secure contact of the EEGElectrode 801 with the scalp of the Patient 602. The Electrode PressureMechanism 802 consists of any of a spring mechanism, inflatablebladder(s), hydraulic plunger(s) and the like, which apply mechanicalpressure to the “back side” of the EEG Electrodes 801 thereby to forcethem away from the interior surface of the exoskeleton 601 or 701 untilthe EEG Electrodes 801 come into firm contact with the scalp of thePatient 602.

In addition, the EEG Processing Unit 600 can include optional automatedContact Enhancer Mechanism 803, which provides movement and/or rotationof the EEG Electrode 801 to clean the skin of the Patient 602 tooptimize the electrode contact with the Patient's scalp as indicated bythe measured impedance (described with respect to FIGS. 10 and 11).

EEG Electrode Placement

FIG. 9 is a block diagram illustrating an example electrode placementfor gathering EEG data and represents electrode placement consistentwith the International 10-20 EEG Classification System. Each electrodesite has a letter to identify the lobe and a number or another letter toidentify the hemisphere location. The letters C, F, Fp, O, P, and Tstand for Central, Frontal, Frontal Pole, Occipital, Parietal, andTemporal locations of the brain, respectively. The even numbers refer tolocations in the right hemisphere, the odd numbers refer to locations inthe left hemisphere, and the letter “z” refers to an electrode placed onthe midline. It is evident that, due to the number of the electrodes,the test operator must carefully associate each electrode with itspredefined site on the Patient's head and ensure good physical contactof the electrode with the scalp before initiating the EEG test.

EEG Electrode

FIG. 10 illustrates a circuit diagram of the elements incorporated inthe electrodes (603-1 to 603-N and 703-1 to 703-N, which arecollectively denoted as EEG electrode 1000 in this Figure to describe atypical electrode) and the associated transmitter 1022.

The voltages generated by the EEG sensor 1001 contained in the EEGElectrode 1000 are amplified and filtered before being transmitted to ananalysis platform, which can be a Physician's laptop, either wirelesslyor via a set of tethering wires. The EEG Processing Unit 600 includesautomatic artifacting which identifies when there is sufficient cleandata compiled in the testing session. This process eliminates muscle orother physical artifact-related voltages. Clean data, which representsreal brain voltages as opposed to muscle or physical artifact relatedvoltages, are thereby produced. The apparatus includes automatic motionartifacting via an accelerometer that produces data which enhances theidentification of accurate data.

The data collected by the sensors can be over sampled to enable a filterto effectively separate the signal from the noise. Over sampling is onlyperformed on the pass-band information and not all of the data. Onereason for over sampling only on the pass-band information is that it isnot necessary to communicate all of the data but only the data in thepass-band. In traditional applications, in which the filtering wasperformed after the raw data was transmitted to a remotely locatedprocessor, all of the data was over sampled and sent over thecommunications channel. The use of over sampling and filtering in theEEG Processing Unit 600 reduces the bandwidth requirements of the datalink and results in a cost savings over traditional systems.Furthermore, this architecture results in processing data with asignal-to-noise ratio that is lower than traditional systems.Consequently, the need for the use of conductive fluid on the sensor1001 can be reduced or even eliminated in some cases.

FIG. 10 is a block diagram illustrating the layout of various componentsof the EEG Electrode 1000, which includes: EEG electrode signals 1001;calibration signals 1002; switch 1003; impedance check 1004; the filters1006, 1009; the chain of amplifiers 1007, 1008; Analog to Digital (A/D)converters 1005, 1010, 1012; optional accelerometer 1011; optionalelectrode motor 1025; and microcontroller 1021, all located in orproximate to EEG Electrode 1000 as shown in the Figures. TheAnalog-Digital Converters 1005, 1010, 1012 and the microcontroller 1021can be part of the same electronic chip. One advantage of placing themicrocontroller 1021 in the EEG Electrode 1000 assembly is that the datarate of the digital communications is kept to a minimum. In addition,the data processing task is distributed, simplifying the EEG ProcessingUnit 600 and, consequently, the cost.

FIG. 11 illustrates in flow diagram form the operation of the EEGProcessing Unit 600. At step 1101, after the EEG Processing Unit 600 hasbeen placed on the head of the Patient 602 and activated, the EEGElectrode 1000 generates analog electrode signals which contain multiplecomponents: EEG signals, artifacts, and impedance measurements. The EEGvoltages in electrode signals 501 can be replaced by calibration signals502 generated by signal generators. Test waveforms are generated insoftware and then output as calibration signals 502, which areartificial representations of standard EEG, in both shape and voltageamplitudes, for the purpose of calibration and testing. In addition,accelerometer 511 generates motion signals indicative of the movement ofthe framework in three dimensions. A number of data processing stepsoperate on the EEG data to produce processed EEG data. In particular, atstep 1102, impedance measurement device 1004 measures the impedance ofthe EEG sensor 1000 which is indicative of the attachment of the EEGsensor 1000 to the scalp of the Patient 102. Impedance is measured byapplying a small AC voltage between each scalp electrode and the groundelectrode and measuring the resultant peak-peak voltage. The results ofthis test are processed by A/D Converter 1005 and transmitted by themicrocontroller 1021 to the user interface 1024 for display viatransmitter 1022 and receiver 1023 to enable the test operator todetermine whether to proceed with the data collection process orreadjust the EEG sensors 1000. Alternatively, an automated electrode fitprocess can be executed, where the impedance values are fed to themicrocontroller 1021, which forwards these values (or other controlsignals) to the associated electrode positioning motors 1025. Theelectrode positioning motor voltage and/or current readings are returnedto the microcontroller 1021; and if the measured impedance value waslow, the electrode positioning motor 1025 (a servo or stepping type ofmotor) moves to reposition the electrode on the scalp. The adjustmentcycle continues until the specified impedance value is reached.Alternatively, inward motion of the electrode onto the scalp createspressure; and the sensed electrode positioning motor drive current orvoltage is monitored until the pressure cutoff value is reached, asindicated by the measured electrode positioning motor drive current orvoltage.

At step 1103, the presence of motion is determined by accelerometer 1011generating signals indicative of three-dimensional motion. Theaccelerometer 1011 output is processed by A/D Converter 1012 andtransmitted by the microcontroller 1021 to the user interface 1024 fordisplay via transmitter 1022 and receiver 1023 to enable the testoperator to determine whether to proceed with the data collectionprocess or readjust the EEG sensors 1001.

At step 1104, artifacts in the EEG data are processed by transmittingthese analog signals via switch 1003 to high pass filter 1006 to removeDC components of the EEG data and out-of-band signals. Pre-Amplifier1007 and Amplifier 1008 increase the magnitude of the EEG data signals,and these then are filtered by Amplifier Filter 1009 before beingconverted to DC signals by A/D Converter 1010. This processing issupplemented by software in microcontroller 1021 where the EEG data thenis processed by artifact-removal software to remove artifacts (e.g.,electrical signals from muscle movement) to ensure that proper data wascollected. Artifact detection serves three purposes: the first is toallow the administrator to instruct the patient when muscle-relatedartifacts are overwhelming the signal (for example, excessive eyemovement or muscle tension); the second is to inform the administratorwhen enough clean data has been obtained and test is complete; and thethird is for post-hoc data analysis which may include identification ofclean epics or the cleaning of contaminated epics. The microcontroller1021 automatically determines whether the data is of adequate qualityfor transmission to the user interface 1024 for display via transmitter1022 and receiver 1023. The processed EEG data 1105 (clean brain wavevoltages) then is received by the Physician Application 610 where it isstored in memory 612 for later display by the Physician for analysis anddiagnosis.

In response to receiving sets of EEG data relating to a Patient, a dataselection physician interface screen can be displayed that allows thePhysician to select a desired data manipulation process to use on thecollected data as illustrated diagrammatically in FIG. 5. In some cases,the data selection physician interface screen allows the Physician toselect a portion of the data collected which is analyzed and/ordisplayed. For example, if a large amount of EEG data is collected undera variety of test conditions, the Physician could select the portion ofthe EEG data for analysis that is desired by the Physician.

Steps To Create A Patient/Condition Database

In order to have a baseline reference for manipulating the Patient datacollected as part of a test, such as the EEG test described above, theremust be a statistically valid compilation of relevant patent data whichcan be used to identify conditions/ailments. This compilation isgenerated by collecting reference data (referred to as control databasein FIG. 2) on ‘N’ conditions for M>>1 patients per condition. Conditionscan include ailments, ailment risks, performance categories, behaviorcategories, intervention categories, single or multiple device orphysiological test results, and/or individual patient identification.The data can include EEG data placed into a voltage vs. time matrix,with the number of dimensions being the number of EEG channels. Forexample, as noted above, EEG voltage readings can be collected from 19scalp sites, sampled at 120 Hz for 100 sec, on a number of patients whotest normal regarding brain function. These 12,000 data points from eachpatient can become part of a normal reference. EEG data can also becollected on patients who have been diagnosed with Alzheimer's and thisbecomes a second reference. EEG data can be collected on patients whohad a recent concussion from an auto accident, and these become a thirdreference, etc.

The data collection process may include outcome-based “auto artifacting”to eliminate spurious voltage readings that are not related to brainreadings. For example, the three major types of artifacts in current19-channel EEG data sets are caused by eye movement, body movement, andneck and facial muscle tension. An additional class of artifacts may becaused by poor electrode contact or equipment malfunction. The presenceof these artifacts compromises the performance of certain algorithms, aswell as other methods of state discrimination. These artifacts arecharacterized by EEG waveforms with a typical morphology anddistribution which can be detected in the frequency domain. The presenceof artifacts in EEG data tends to a) reduce the ability to discriminatebetween EEG acquired in different states, and b) obscure similaritiesbetween data sets acquired in the same state.

To minimize the effect of the above-mentioned kinds of EEG artifacts,multichannel spectral thresholding can mark EEG file epochs as“included” or “deleted”. The automated EEG artifacting algorithm maythen be “re-tuned” using new objective functions, depending on the“outcome” or the targeted EEG parameters being measured. For example, araw-wave spike analysis could require a different level of artifactingdetail than a parameter derived from a spectral analysis of differentfrequencies such as a Fourier Transform. These artifacting techniquesinclude Laplacian Eigenvalue techniques applied to new target data setsassociated with more reliable diagnostic information, and may employ ICAand/or other signal processing techniques for more accurate artifactdetection.

Data Is Compiled Into A Single Archetype Dataset For Each Condition

The collected patient data then is compiled into a single archetypedataset for each condition. The compilation may include normalization oraveraging for outlier exclusion or to establish boundary values.

For example, the 12,000 data points (not necessarily time continuous)from the patient in the normal reference (above) can each be placed into19-dimensional space, with each dimension equal to the voltage readingfrom a particular electrode, and placed into a voltage vs. timereference. These can be stored as a 19×12,000 matrix, where the numberof rows equals the number of electrodes (only 2 columns shown for thesake of simplicity):

$\begin{matrix}{{Volts}\mspace{14mu}\left( {{{time}\mspace{14mu} 1},{{channel}\mspace{14mu} 1}} \right)} & {{Volts}\mspace{14mu}\left( {{{time}\mspace{14mu} 2},{{channel}\mspace{14mu} 1}} \right)} \\{{Volts}\mspace{14mu}\left( {{{time}\mspace{14mu} 1},{{channel}\mspace{14mu} 2}} \right)} & {{Volts}\mspace{14mu}\left( {{{time}\mspace{14mu} 2},{{channel}\mspace{14mu} 2}} \right)} \\\ldots & \ldots \\{{Volts}\mspace{14mu}\left( {{{time}\mspace{14mu} 1},{{channel}\mspace{14mu} 19}} \right)} & {{Volts}\mspace{14mu}\left( {{{time}\mspace{14mu} 2},{{channel}\mspace{14mu} 19}} \right)}\end{matrix}$Establish Biomarker To Define Each Archetype

In order to uniquely define each archetype, biomarkers can be used. Abiomarker is anything that can be used as an indicator of a particulardisease state or some other biological state of an organism. Biomarkersare characteristic biological properties that can be detected andmeasured in parts of the body like the blood or tissue. Disease-relatedbiomarkers give an indication of whether there is a threat of disease(risk indicator or predictive biomarkers), if a disease already exists(diagnostic biomarker), or how such a disease may develop in anindividual case (prognostic biomarker). For chronic diseases, whosetreatment may require patients to take medications for years, accuratediagnosis is particularly important, especially when strong side effectsare expected from the treatment. In these cases, biomarkers are becomingmore and more important, because they can confirm a difficult diagnosisor even make it possible in the first place. A number of diseases, suchas Alzheimer's disease or rheumatoid arthritis, often begin with anearly, symptom-free phase. In such symptom-free patients, there may bemore or less probability of actually developing symptoms. In thesecases, biomarkers help to identify high-risk individuals reliably and ina timely manner so that they can either be treated before onset of thedisease or as soon as possible thereafter. Biomarkers may result fromEigenvector decomposition of the above-noted data Matrix, or statisticalpattern recognition techniques can be used. Error spreads may bedeveloped to establish boundaries for archetype definitions, since arange of values is typically necessary to define the archetype.

For example, all patients for each archetype can be placed into a singlematrix. The 3 Eigenvectors of each of the 3 archetypes (noted above asexamples) that are the most significant biomarkers for each archetypecan be used to define the 3 of the 19 dimensions that are mostsignificant. The data points from each of the 3 archetypes can bedisplayed against each other to define the biomarkers. Comparisons canbe visual or as a percentage of points that match.

Eigenvectors

In mathematics, eigenvalue, eigenvector, and eigenspace are relatedconcepts in the field of linear algebra. Eigenvalues, eigenvectors andeigenspaces are properties of a matrix. They are computed by a methoddescribed below, give important information about the matrix, and can beused in matrix factorization. In general, a matrix acts on a vector bychanging both its magnitude and its direction. However, a matrix may acton certain vectors by changing only their magnitude, and leaving theirdirection unchanged (or possibly reversing it). These vectors are theeigenvectors of the matrix. A matrix acts on an eigenvector bymultiplying its magnitude by a factor, which is positive if itsdirection is unchanged and negative if its direction is reversed. Thisfactor is the eigenvalue associated with that eigenvector. An eigenspaceis the set of all eigenvectors that have the same eigenvalue. Theconcepts cannot be formally defined without prerequisites, including anunderstanding of matrices, vectors, and linear transformations. Thetechnical details are given below.

Eigenvector—Mathematical Definition

In linear algebra, there are two kinds of objects: scalars, which arejust numbers; and vectors, which can be thought of as arrows, and whichhave both magnitude and direction (though more precisely a vector is amember of a vector space). In place of the ordinary functions ofalgebra, the most important functions in linear algebra are called“linear transformations”; and a linear transformation is usually givenby a “matrix”, an array of numbers. Thus instead of writing f(x) wewrite M(v) where M is a matrix and v is a vector. The rules for using amatrix to transform a vector are given in the article linear algebra.

If the action of a matrix on a (nonzero) vector changes its magnitudebut not its direction, then the vector is called an eigenvector of thatmatrix. A vector which is “flipped” to point in the opposite directionis also considered an eigenvector. Each eigenvector is, in effect,multiplied by a scalar, called the eigenvalue corresponding to thateigenvector. The eigenspace corresponding to one eigenvalue of a givenmatrix is the set of all eigenvectors of the matrix with thateigenvalue.

EXAMPLE

If a matrix is a diagonal matrix, then its eigenvalues are the numberson the diagonal and its eigenvectors are basis vectors to which thosenumbers refer. For example, the matrix

$\quad\begin{bmatrix}3 & 0 \\0 & 0.5\end{bmatrix}$stretches every vector to three times its original length in thex-direction and shrinks every vector to half its original length in they-direction. Eigenvectors corresponding to the eigenvalue 3 are anymultiple of the basis vector [1, 0]; together they constitute theeigenspace corresponding to the eigenvalue 3. Eigenvectors correspondingto the eigenvalue 0.5 are any multiple of the basis vector [0, 1];together they constitute the eigenspace corresponding to the eigenvalue0.5. In contrast, any other vector, [2, 8] for example, will changedirection. The angle [2, 8] makes with the x-axis has a tangent of 4,but after being transformed, [2, 8] is changed to [6, 4], and the anglethat vector makes with the x-axis has a tangent of ⅔.

Linear transformations of a vector space, such as rotation, reflection,stretching, compression, shear, or any combination of these, may bevisualized by the effect they produce on vectors. In other words, theyare vector functions. More formally, in a vector space L, a vectorfunction A is defined if for each vector x of L there corresponds aunique vector y=A(x) of L. For the sake of brevity, the parenthesesaround the vector on which the transformation is acting are oftenomitted. A vector function A is linear if it has the following twoproperties:Additivity: A(x+y)=Ax+AyHomogeneity: A(αx)=αAxwhere x and y are any two vectors of the vector space L and oc is anyscalar. Such a function is variously called a linear transformation,linear operator, or linear endomorphism on the space L.

Given a linear transformation A, a non-zero vector x is defined to be aneigenvector of the transformation if it satisfies the eigenvalueequation Ax=λx for some scalar λ. In this situation, the scalar λ iscalled an eigenvalue of A corresponding to the eigenvector x.

The key equation in this definition is the eigenvalue equation, Ax=λx.That is to say that the vector x has the property that its direction isnot changed by the transformation A, but that it is only scaled by afactor of λ. Most vectors x will not satisfy such an equation; a typicalvector x changes direction when acted on by A, so that Ax is not amultiple of x. This means that only certain special vectors x areeigenvectors, and only certain special scalars λ are eigenvalues. Ofcourse, if A is a multiple of the identity matrix, then no vectorchanges direction, and all non-zero vectors are eigenvectors.

Factor Analysis

In factor analysis, the eigenvectors of a covariance matrix orcorrelation matrix correspond to factors, and eigenvalues correspond tothe variance explained by these factors. Factor analysis is astatistical technique used to deal with large quantities of data. Theobjective is to explain most of the covariability among a number ofobservable random variables in terms of a smaller number of unobservablelatent variables called factors. The observable random variables aremodeled as linear combinations of the factors, plus unique varianceterms. Eigenvalues are used in analysis used by Q-methodology software;factors with eigenvalues greater than 1.00 are considered significant,explaining an important amount of the variability in the data, whileeigenvalues less than 1.00 are considered too weak, not explaining asignificant portion of the data variability.

Physician Application Setup

FIG. 4 is a flow chart illustrating operations for setting up orupdating a Physician Application 150. In FIG. 4, a physician firstdetermines a potential ailment or condition of interest at step 410. Forexample, when a patient arrives at a physician's office, the physiciancan make an initial assessment of the patient and determine that thepatient may have a certain ailment or condition (e.g., depression). Inother situations, the physician could have decided that he wants toscreen all his patients for a certain ailment (e.g., attention deficitdisorder). Still yet, selections of certain ailments could occurautomatically. For example, the physician could select that all previousdiagnoses in the patient's electronic medical records be retested and/ormonitored.

Upon deciding ailment(s) of interest, the physician can set up (program)the Physician Application 150, or load a pre-existing interface setup,such as using the biomarkers as described below, to determine if thepatient test data is indicative of the ailment of interest. To this end,the physician also can access the Digital Library 105 and search at step420 for the ailment of interest (e.g., depression). The search typicallyreturns published literature which provides data relations at step 430that, when statistically compared to a base group of control subjects,has been shown to indicate the ailment. The physician can review thepublished literature and decide at step 440 whether to accept or rejectthe data relations in a particular article. If the physician rejects thedata relation, then the Physician Application 150 is not updated at step450. In addition to searching for published literature which indicatesdata relations for a particular ailment, the physician can use personalknowledge about patient test data to set up data relations to becompared to the control group at step 470. Still yet, the physician cansearch for and load pre-designed expert interfaces (biomarkers) withdata relations that have been developed by experts in the field at step480. Whether the physician selects data relations from the DigitalLibrary 105, enters data relations based on personal knowledge, and/oraccepts a pre-designed expert interface, the application interface isappropriately updated.

Use Of Biomarkers On Patient Data

The creation of biomarkers provides the physician with the tools thatcan be used to analyze complex and voluminous collected patient data inorder to identify one or more conditions relating to the patient. FIG. 5illustrates diagrammatically the use of the biomarker concept. The planeon the left of the Figure labeled “Data” represents a data matrix ofcollected patient data points (shown as the “X”s in the 11 Rows R1-R11and 11 columns C1-C11 of FIG. 5 for illustrative purposes) for aselected patient and stored in Patient_Test_Data Module 340. This can betest measurement data, such as the above-described EEG data, which canoptionally include patient characterization data (mentioned above),which can also optionally include data from related tests as stored inMedical Data Input Module 360.

The physician can manipulate the collected patient data (DATA) withPhysician Diagnosis Module 390 at step 1601 by retrieving the storedcollected patient test data from memory, such as Patient_Test_DataModule 340 and using any of a number of predefined mathematical and/orlogical processes, termed “biomarkers” herein. At step 1602, thephysician can select one or more desired biomarkers (Process A, ProcessB), which are used to manipulate the collected patient data (DATA). Theorder of data manipulation by these biomarkers is also selected by thephysician at step 1603, with series or parallel or combinations ofseries and parallel processing being possible. In FIG. 5, the biomarkerdata manipulation processes (Process A, Process B) are illustrated asplanes which are parallel to the collected patient data plane (DATA).The data flows from left to right, with each data point (X) in thematrix of collected patient data (DATA) being operated on (in some casesignored) at step 1604 by each successive biomarker process (Process A,Process B) to produce “O”s, then “□”s; thence the resultant output dataset (DISPLAY) of “D”s which is indicative of the biomarker(s) extractedfrom the collected patient data is stored at step 1605 by the PhysicianDiagnosis Module 390. By comparing the extracted biomarker(s) withstandard biomarkers associated with known ailments, the physician canascertain what ailment(s) are likely impacting the selected patient.This comparison can be executed by Physician Diagnosis Module 390 atstep 1606, or the biomarker results can be presented at step 1607 asmulti-dimensional visualizations which can be concurrently displayed atstep 1608 on display 120 with standard visualizations of biomarkersassociated with ailments.

Therefore, the physician can access a library of biomarker datamanipulation processes and test the diagnosis hypothesis on thecollected patient data (DATA) to see what results are obtained. When theoutput data (DISPLAY) produces a recognizable pattern matching a knownset of biomarkers for a defined ailment, the physician can then haveconfidence that their hypothesis is credible. Since the collectedpatient data (DATA) is unlikely to have a 100% correlation with anyselected biomarker, the biomarkers can be defined as “fuzzy filters”which have a range of typical values or weights assigned to variousprocesses and/or data points thereby to capture ailments where the datamay not be conclusive, but also not totally reject data that are notaccurate matches to the known ailment archetype data.

In some cases, the physician selects only a portion of the datacollected to be analyzed and/or displayed. For example, if a largeamount of EEG data is collected under a variety of test conditions, thephysician could select the portion of the EEG data for analysis that isdesired by the physician.

The collection of biomarkers for ailments can be stored in the PatientData Management System 300 or can be stored in whole or in part in theDigital Library 105 for retrieval by the physician. The Digital Libraryphysician interface screen can be generated that also presents articles,links to articles, summaries of articles, or other published informationretrieved from the Digital Library 105. The Digital Library physicianinterface screen allows the physician to search Digital Library 105 fordata relations relating to particular ailments and/or conditions ofinterest to the physician. For example, the physician could search fordata relations which might indicate Alzheimer's, ADD, depression, or thelike when statistically compared to the normal EEG data stored innormative database 260. Once an analysis of the data relations has beenperformed, the Report Physician Interface Screen can be displayed on theterminal. The Report Physician Interface Screen could include apredictive ailment report containing a list of potential mental orphysical ailments and/or a treatment report containing treatment plansfor the list of potential mental or physical ailments in the predictiveailment report. The Diagnostic Physician Interface Screen can bedisplayed on the terminal with input and/or selection areas that allowsfor a physician to input a diagnosis, the physician's reasoning, and/orprescribed treatment plans.

Typical EEG Output Data Visualizations

FIG. 13A is a screen shot of patient EEG test data presented in aspectral array that may be presented. To generate the spectral arrayshown in FIG. 13A, the raw EEG waves for each electrode are transformedinto magnitude or power spectrums. From this type of display, a trainedclinician could derive the distribution of power and amplitude(strength) of the brainwaves at each site. FIG. 14B is a screen shot oftopographic maps of the raw EEG data that may be presented. Asillustrated in FIG. 14B, the topographic maps summarize EEG data byrepresenting power values (i.e., voltage variations) in selectedfrequencies at selected electrode sites.

FIG. 15 illustrates a screen shot showing a compressed spectral array ofraw EEG data that may be presented. The compressed spectral arrayillustrates how the spectrum of EEG evolves over time and can be usefulfor demonstrating certain trends in the data over time which is notobservable in many of the other display formats.

Summary

The Patient Data Management System enables a physician to selectpredetermined (or physician defined) data manipulation processes,illustrative of predetermined ailment biomarkers, to be used inmanipulating collected patient data to thereby obtain results which canbe visualized to confirm or deny the validity of the physician'sdiagnosis.

What is claimed as new and desired to be protected by Letters Patent ofthe United States is:
 1. A physician operated medical data analysissystem for assisting a physician in identifying ailments and conditionsrelating to an identified patient, comprising: a memory for storing: aplurality of sets of patient medical data relating to an identifiedpatient, which patient medical data comprises a plurality ofmeasurements of biomarkers that correspond to a biological property ofone or more organisms in the patient, a plurality of sets ofailment-specific biomarkers, each of which correspond to a set ofcharacteristic biological properties that can be detected and measuredin a patient, and each of which are indicative that an organism isafflicted with a predetermined ailment, and a plurality of datamanipulation modules, each defining at least one process fortransforming patient medical data pursuant to a predefined schema toproduce a set of biomarkers that are indicative of selected biologicalproperties of at least one organism in the patient; a patient medicaldata processor, responsive to a physician selecting one of saidplurality of stored sets of patient medical data, a plurality of thestored data manipulation modules and an order of applying the selectedplurality of stored data manipulation modules to the selected stored setof patient medical data, for processing the selected patient medicaldata using the selected data manipulation modules in the selected orderto produce an output set of patient-specific biomarkers that areindicative of biological properties of at least one organism in thepatient; a patient data manipulation module, responsive to the physicianselecting a plurality of sets of ailment-specific biomarkers via thephysician terminal device, configured to identify said patient-specificbiomarkers in said output set of patient-specific biomarkers thatcorrelate with each of the biomarkers in each of the selected sets ofailment-specific biomarkers; and a display device for displaying on aphysician terminal device for visual comparison, a visualization of eachof the plurality of sets of ailment-specific biomarkers selected by thephysician and a visualization of the identified patient-specificbiomarkers.
 2. The medical data analysis system of claim 1 wherein theset of patient medical data comprises: monitoring data collected frommedical devices operable to measure physiological data relating to theidentified patient.
 3. The medical data analysis system of claim 1wherein each of the plurality of data manipulation modules comprises: atleast one mathematical or logical data manipulation schema fortransforming an input set of data pursuant to predefined datamanipulation rules to identify a correspondence of a predeterminedbiomarker with patient medical data in the selected set of patientmedical data.
 4. The medical data analysis system of claim 1 wherein thepatient medical data processor comprises: a condition identificationprocess, for generating an indication of a degree of correspondencebetween a predetermined biomarker associated with the data manipulationmodule and the selected set of patient medical data to identify aprobability of a condition defined by the selected data manipulationmodule being present in the selected patient.
 5. The medical dataanalysis system of claim 1, further comprising: a comparative displaygenerator for generating a visualization of both the selected patientmedical data and the predetermined biomarker associated with acondition.
 6. A method of operating a physician operated medical dataanalysis system for assisting a physician in identifying ailments andconditions relating to an identified patient, comprising: storing in amemory: at least one set of patient medical data relating to anidentified patient, which patient medical data comprises a plurality ofmeasurements of biomarkers that correspond to a biological property ofone or more organisms in the patient, a plurality of sets ofailment-specific biomarkers, each of which correspond to a set ofcharacteristic biological properties that can be detected and measuredin a patient, and each of which are indicative that an organism isafflicted with a predetermined ailment, and enabling a physician toaccess, via a user interface communicatively connected to a server, aplurality of data manipulation modules, each defining at least oneprocess for transforming said patient medical data pursuant to apredefined schema to produce a set of biomarkers that are indicative ofselected biological properties of at least one organism in the patient;processing in a server, in response to a physician selecting a set ofpatient medical data, a plurality of the data manipulation modules andan order of applying the selected data manipulation modules to theselected patient medical data, the selected patient medical data usingthe selected data manipulation modules in the selected order to producean output set of patient-specific biomarkers that are indicative ofbiological properties of at least one organism in the patient; identify,in response to the physician selecting a plurality of sets ofailment-specific biomarkers via the physician terminal device, saidpatient-specific biomarkers in said output set of patient-specificbiomarkers that correlate with each of the biomarkers in each of theselected sets of ailment-specific biomarkers; and presenting, on adisplay communicatively connected to the server, a visualization of eachof the plurality of sets of ailment-specific biomarkers selected by thephysician and a visualization of the output set of patient-specificbiomarkers.
 7. The method of operating a medical data analysis system ofclaim 6, further comprising: collecting monitoring data from medicaldevices operable to measure physiological data relating to theidentified patient.
 8. The method of operating a medical data analysissystem of claim 6 wherein the step of processing patient medical datacomprises: transforming, using at least one mathematical or logical datamanipulation schema defined by the data manipulation module, an inputset of data pursuant to predefined data manipulation rules to identify acorrespondence of a predetermined biomarker in the selected set ofpatient medical data.
 9. The method of operating a medical data analysissystem of claim 8 wherein the step of processing patient medical datacomprises: generating an indication of a degree of correspondencebetween a predetermined biomarker associated with the data manipulationmodule and the selected set of patient medical data to identify aprobability of a condition defined by the selected data manipulationmodule being present in the selected patient.
 10. The method ofoperating a medical data analysis system of claim 6, further comprising:generating a visualization of both the selected patient medical data andthe predetermined biomarker associated with a condition.
 11. A physicianoperated medical data analysis system for assisting a physician inidentifying ailments and conditions, relating to an identified patient,via an associated set of patient medical data, comprising: a pluralityof data manipulation modules, each defining at least one process fortransforming said patient medical data relating to an identifiedpatient, which patient medical data comprises a plurality ofmeasurements of biomarkers that correspond to a biological property ofone or more organisms in the patient pursuant to a predefined schema toa set of biomarkers that are indicative of selected biologicalproperties of at least one organism in the patient; a patient medicaldata processor, responsive to a physician selecting a data manipulationmodule for application to a set of patient medical data, for processingthe set of patient medical data using the selected data manipulationmodule to produce an output set of patient-specific biomarkers that areindicative of biological properties of at least one organism in thepatient; a patient data manipulation module, responsive to the physicianselecting a plurality of sets of ailment-specific biomarkers via thephysician terminal device, configured to identify said patient-specificbiomarkers in said output set of patient-specific biomarkers thatcorrelate with each of the biomarkers in each of the selected sets ofailment-specific biomarkers; and a display, communicatively connected tothe patient medical data processor, for presenting a visualization ofeach of the plurality of sets of ailment-specific biomarkers selected bythe physician and a visualization of the output set of patient-specificbiomarkers.
 12. The medical data analysis system of claim 11 wherein theset of patient medical data comprises: monitoring data collected frommedical devices operable to measure physiological data relating to theidentified patient.
 13. The medical data analysis system of claim 11wherein each of the plurality of data manipulation modules comprises: atleast one mathematical or logical data manipulation schema fortransforming an input set of data pursuant to predefined datamanipulation rules to identify a correspondence of a predeterminedbiomarker with patient medical data in the selected set of patientmedical data.
 14. The medical data analysis system of claim 11 whereinthe patient medical data processor comprises: a condition identificationprocess, for generating an indication of a degree of correspondencebetween a predetermined biomarker associated with the data manipulationmodule and the selected set of patient medical data to identify aprobability of a condition defined by the selected data manipulationmodule being present in the selected patient.
 15. The medical dataanalysis system of claim 11, further comprising: a comparative displaygenerator for generating a visualization of both the selected patientmedical data and the predetermined biomarker associated with acondition.
 16. A method of operating a physician operated medical dataanalysis system for assisting a physician in identifying ailments andconditions, relating to an identified patient, via an associated set ofpatient medical data, comprising: enabling a physician to access aplurality of data manipulation modules, each defining at least oneprocess for transforming said patient medical data relating to anidentified patient, which patient medical data comprises a plurality ofmeasurements of biomarkers that correspond to a biological property ofone or more organisms in the patient pursuant to a predefined schema toa set of biomarkers that are indicative of selected biologicalproperties of at least one organism in the patient; processing in aserver, in response to a physician selecting a data manipulation modulefor application to a set of patient medical data, for processing the setof patient medical data using the selected data manipulation module toproduce an output set of patient-specific biomarkers that are indicativeof biological properties of at least one organism in the patient; andpresenting, on a display communicatively connected to the server, avisualization of each of the plurality of sets of ailment-specificbiomarkers selected by the physician and a visualization of the outputset of patient-specific biomarkers.
 17. The method of operating amedical data analysis system of claim 16, further comprising: collectingmonitoring data from medical devices operable to measure physiologicaldata relating to the identified patient.
 18. The method of operating amedical data analysis system of claim 16 wherein the step of processingpatient medical data comprises: transforming, using at least onemathematical or logical data manipulation schema defined by the datamanipulation module, an input set of data pursuant to predefined datamanipulation rules to identify a correspondence of a predeterminedbiomarker in the selected set of patient medical data.
 19. The method ofoperating a medical data analysis system of claim 18 wherein the step ofprocessing patient medical data comprises: generating an indication of adegree of correspondence between a predetermined biomarker associatedwith the data manipulation module and the selected set of patientmedical data to identify a probability of a condition defined by theselected data manipulation module being present in the selected patient.20. The method of operating a medical data analysis system of claim 16,further comprising: generating a visualization of both the selectedpatient medical data and the predetermined biomarker associated with acondition.